Part of the Standalone Models by Convergent Intelligence LLC: Research Division This model is part of the Convergent Intelligence LLC: Research Division portfolio.
Model Card
By Convergent Intelligence, published under apache-2.0, revision e7a1d5565e15.
Part of the Standalone Models by Convergent Intelligence LLC: Research Division This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts: For the full mathematical treatment, see Discrepancy Calculus: Foundations and Core…
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By Convergent Intelligence LLC: Research Division
Convergent Intelligence Portfolio
Part of the Standalone Models by Convergent Intelligence LLC: Research Division
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Total Portfolio: 41 models | 2,781 total downloads
Last updated: 2026-03-28 12:58 UTC
Discrepancy Calculus Foundation
This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces.
DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts:
- Discrepancy Operator (D): Measures the gap between expected and observed behavior at each training step
- Jump Sets: Boundaries where model behavior changes discontinuously — these are features, not bugs
- Ghost Imprinting: Teacher knowledge that transfers to student models through weight-space topology rather than explicit distillation signal
For the full mathematical treatment, see Discrepancy Calculus: Foundations and Core Theory (DOI: 10.57967/hf/8194).
Citation chain: Structure Over Scale (DOI: 10.57967/hf/8165) → Three Teachers to Dual Cognition (DOI: 10.57967/hf/8184) → Discrepancy Calculus (DOI: 10.57967/hf/8194)
Identity and Version
- Repository
- reaperdoesntknow/S-AGI
- Publisher
- Convergent Intelligence
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- e7a1d5565e15b9a054dbbabceb87c81c9363a4ee
- First published
- 2026-01-21
- Last updated
- 2026-09-18
Files and Weights
2 files, 4.6 KB in total.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| README.md | Documentation | 3.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
Released by Convergent Intelligence through its official repository on Hugging Face. Read the license.
Questions About S-AGI
Can I use S-AGI commercially?
Yes. S-AGI is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.